Changes in usage patterns by customers of mobile wireless communications services, with an increased emphasis on smart phone-based data traffic as opposed to voice, have placed unprecedented demand upon underlying physical network infrastructures that support such services. Proliferation of smart phones, and their subsequent use to carry out high volume/data-rate communications—including streaming video transmissions—has resulted in exponential growth in the volume of data flowing over wireless networks. The substantial increased data transmission volume via existing physical networks is challenging the capabilities of the infrastructure to a degree that was not contemplated when mobile wireless services were primarily used to support voice communications. The increased volume of data communications presents a challenge for service providers who must ensure reliable mobile wireless service for most, if not all, users.
Moreover, users have become accustomed to receiving mobile wireless service at unprecedented levels of quality and reliability. The high degree of reliability achieved by mobile wireless services has resulted in many mobile wireless subscribers foregoing conventional landline service. Such subscribers rely wholly upon mobile wireless service to meet their communication needs, or at least to meet their voice communications needs. Given the increased reliance of subscribers, it is imperative for the underlying mobile wireless network infrastructure to be properly maintained. When parts of the mobile wireless network infrastructure are unable to adequately support subscriber needs at particular locations in the network, such parts (e.g., cell sites or portions thereof) must be identified. Thereafter, the cause(s) of the identified performance failure need to be identified, and solutions are proposed.
The ability to accurately forecast, with a satisfactory level of precision, data throughput demand at various physical points within a mobile wireless network at particular points in time ensures that proper resources are committed by a mobile network service provider to meet user needs. Key Performance Indicators (KPIs) are a type of information used to measure the performance and capacity of wireless networks. Actual data throughput demand, and the mobile wireless network's ability to meet the data throughput demand are important Key Performance Indicator (KPI) types. Examples of other KPI types include: data sessions, transactions per unit time (e.g., second), disk usage, CPU usage, memory usage, data attempts, data sessions, data volume, and messages per unit time. Vast quantities of KPI data points are acquired for processing/analyzing during the course of a period of review for purposes of analyzing user data throughput demand on various mobile wireless network components and the mobile wireless network components' ability to meet such demand.
Network service providers do not have unlimited access to resources for addressing every need evidenced by acquired KPI's. A challenge to maintaining a mobile wireless data network, based upon monitored performance and capacity measurements, is to ensure proper allocation of limited resources for repairing and/or upgrading existing network infrastructure components. However, identifying the cause of poor data transmission service and the remedy for the poor service is not a trivial endeavor. Several challenges to identifying anomalous data points are discussed below.
A first challenge, to conducting a meaningful analysis and proposing a beneficial long term response to identified problems/needs in a mobile wireless data network, is the massive volume of raw network performance (e.g., KPI) data acquired by various components of the mobile wireless network. During the course of an evaluation period, a mobile wireless data network management system acquires millions of potentially useful data points for processing. It would take years for such information to be evaluated manually. Therefore, some form of automated evaluation process is essential.
Another challenge involves determining the relevance of individual data points acquired during a period of interest. The raw data itself merely provides a set of performance “facts.” The information itself is incapable of specifying whether a particular mobile wireless data network component is performing satisfactorily and/or whether remedial actions are needed. Thus, standards are generally formulated and applied to the acquired performance data.
Yet another challenge involves identification of performance data trends. Over time, the volume of data at any given node or portion of a mobile wireless network can, and likely does, change. Thus, when a data standard/threshold for forecasting mobile wireless data network system needs is established, that data standard may need to be adjusted over time to address the dynamic nature of user demand as well as any other time-dependent change to observed performance parameters of a mobile wireless data network. Building in a time variant aspect to forecasted performance parameters ensures longevity of the models used by computerized/automated mobile wireless data network performance forecasting systems.